This document discusses how to read and parse XML files in R. It explains that the XML package can be used to read XML files and store them as lists in R. It then provides examples of how to parse an XML file, extract the root node, get the number of nodes, view details of individual nodes, extract element values from nodes, and convert an XML file to a data frame for further processing and analysis.

1. R-XML Files
XML is a file format which shares both the file format and the data on the World Wide
Web, intranets, and elsewhere using standard ASCII text. It stands for Extensible
Markup Language (XML). Similar to HTML it contains markup tags. But unlike HTML
where the markup tag describes structure of the page, in xml the markup tags
describe the meaning of the data contained into he file.
You can read a xml file in R using the "XML" package. This package can be installed
using following command.
install.packages("XML")
Input Data
Create a XMl file by copying the below data into a text editor like notepad. Save the
file with a .xml extension and choosing the file type as all files(*.*).
<RECORDS>
<STUDENT>
<ID>1</ID>
<NAME>Rick</NAME>
<DOB>1/1/2012</DOB>
<YEAR>1</YEAR>
<MARK>90</MARK>
</STUDENT>
<STUDENT>
<ID>2</ID>
<NAME>Rick</NAME>
<DOB>1/1/2012</DOB>
<YEAR>1</YEAR>
<MARK>50</MARK>
</STUDENT>
<STUDENT>
<ID>3</ID>
<NAME>Rick</NAME>
<DOB>1/1/2012</DOB>
<YEAR>1</YEAR>
<MARK>70</MARK>
</STUDENT>
</RECORDS>
Reading XML File
The xml file is read by R using the function xmlParse(). It is stored as a list in R.
# Load the package required to read XML files.
library("XML")
# Also load the other required package.
library("methods")

2. # Give the input file name to the function.
result <- xmlParse(file = "input.xml")
# Print the result.
print(result)
When we execute the above code, it produces the following result −
1
Rick
623.3
1/1/2012
IT
2
Dan
515.2
9/23/2013
Operations
3
Michelle
611
11/15/2014
IT
4
Ryan
729
5/11/2014
HR
5
Gary
843.25
3/27/2015
Finance
6
Nina
578
5/21/2013
IT
7
Simon
632.8
7/30/2013
Operations
8
Guru
722.5
6/17/2014

3. Finance
Get Number of Nodes Present in XML File
# Load the packages required to read XML files.
library("XML")
library("methods")
# Give the input file name to the function.
result <- xmlParse(file = "input.xml")
# Exract the root node form the xml file.
rootnode <- xmlRoot(result)
# Find number of nodes in the root.
rootsize <- xmlSize(rootnode)
# Print the result.
print(rootsize)
When we execute the above code, it produces the following result −
output
[1] 8
Details of the First Node
Let's look at the first record of the parsed file. It will give us an idea of the various
elements present in the top level node.
# Load the packages required to read XML files.
library("XML")
library("methods")
# Give the input file name to the function.
result <- xmlParse(file = "input.xml")
# Exract the root node form the xml file.
rootnode <- xmlRoot(result)
# Print the result.
print(rootnode[1])
When we execute the above code, it produces the following result −
$EMPLOYEE
1
Rick
623.3
1/1/2012
IT

4. attr(,"class")
[1] "XMLInternalNodeList" "XMLNodeList"
Get Different Elements of a Node
# Load the packages required to read XML files.
library("XML")
library("methods")
# Give the input file name to the function.
result <- xmlParse(file = "input.xml")
# Exract the root node form the xml file.
rootnode <- xmlRoot(result)
# Get the first element of the first node.
print(rootnode[[1]][[1]])
# Get the fifth element of the first node.
print(rootnode[[1]][[5]])
# Get the second element of the third node.
print(rootnode[[3]][[2]])
When we execute the above code, it produces the following result −
1
IT
Michelle
XML to Data Frame
To handle the data effectively in large files we read the data in the xml file as a data
frame. Then process the data frame for data analysis.
# Load the packages required to read XML files.
library("XML")
library("methods")
# Convert the input xml file to a data frame.
xmldataframe <- xmlToDataFrame("input.xml")
print(xmldataframe)
When we execute the above code, it produces the following result −
ID NAME SALARY STARTDATE DEPT
1 1 Rick 623.30 2012-01-01 IT
2 2 Dan 515.20 2013-09-23 Operations
3 3 Michelle 611.00 2014-11-15 IT
4 4 Ryan 729.00 2014-05-11 HR
5 NA Gary 843.25 2015-03-27 Finance
6 6 Nina 578.00 2013-05-21 IT
7 7 Simon 632.80 2013-07-30 Operations

5. 8 8 Guru 722.50 2014-06-17 Finance
As the data is now available as a dataframe we can use data frame related function
to read and manipulate the file.